{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 2",
      "language": "python",
      "name": "python2"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 2
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython2",
      "version": "2.7.3"
    },
    "colab": {
      "name": "mnistNN.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "FvtejS3Z1c_5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.datasets import mnist\n",
        "import keras\n",
        "from keras.layers import Dense\n",
        "from keras.models import Sequential\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b2fSwMu61c_-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UO0PNZhA1dAD",
        "colab_type": "code",
        "colab": {},
        "outputId": "e86a79bb-89dc-41a4-f1ce-cc5080603aaa"
      },
      "source": [
        "first_image = x_train[0]\n",
        "first_image = np.array(first_image, dtype='float')\n",
        "pixels = first_image.reshape((28, 28))\n",
        "plt.imshow(pixels, cmap='gray')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RgBrRshf1dAH",
        "colab_type": "code",
        "colab": {},
        "outputId": "4632a2e5-10d0-4e9a-89cf-a34f1ead1c69"
      },
      "source": [
        "from keras.datasets import mnist\n",
        "\n",
        "# Setup train and test splits\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "print(\"Training data shape: \", x_train.shape) # (60000, 28, 28) -- 60000 images, each 28x28 pixels\n",
        "print(\"Test data shape\", x_test.shape) # (10000, 28, 28) -- 10000 images, each 28x28\n",
        "\n",
        "# Flatten the images\n",
        "image_vector_size = 28*28\n",
        "x_train = x_train.reshape(x_train.shape[0], image_vector_size)\n",
        "x_test = x_test.reshape(x_test.shape[0], image_vector_size)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('Training data shape: ', (60000, 28, 28))\n",
            "('Test data shape', (10000, 28, 28))\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CBbf1RPQ1dAM",
        "colab_type": "code",
        "colab": {},
        "outputId": "cb83d663-5bfd-4032-8920-a5835f4f0421"
      },
      "source": [
        "num_classes = 10\n",
        "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
        "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
        "print(\"First 5 training lables as one-hot encoded vectors:\\n\", y_train[:5])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('First 5 training lables as one-hot encoded vectors:\\n', array([[0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
            "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
            "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
            "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
            "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]], dtype=float32))\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vdsKWREt1dAQ",
        "colab_type": "code",
        "colab": {},
        "outputId": "cbf328fd-1f8a-4e36-94ae-8a611493a4a5"
      },
      "source": [
        "image_size = 784 # 28*28\n",
        "num_classes = 10 # ten unique digits\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "# The input layer requires the special input_shape parameter which should match\n",
        "# the shape of our training data.\n",
        "model.add(Dense(units=32, activation='sigmoid', input_shape=(image_size,)))\n",
        "model.add(Dense(units=num_classes, activation='softmax'))\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_1 (Dense)              (None, 32)                25120     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                330       \n",
            "=================================================================\n",
            "Total params: 25,450\n",
            "Trainable params: 25,450\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qpTxG6X61dAU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer=\"sgd\", loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "history = model.fit(x_train, y_train, batch_size=128, epochs=5, verbose=False, validation_split=.1)\n",
        "loss, accuracy  = model.evaluate(x_test, y_test, verbose=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eY8gYdJu1dAY",
        "colab_type": "code",
        "colab": {},
        "outputId": "ee591fe7-b39c-4587-d94b-61acdecb0b35"
      },
      "source": [
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['training', 'validation'], loc='best')\n",
        "plt.show()\n",
        "\n",
        "print(loss, accuracy)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(0.49380879445075987, 0.8846)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TOr_1WoA1dAc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def create_dense(layer_sizes):\n",
        "    model = Sequential()\n",
        "    model.add(Dense(layer_sizes[0], activation='sigmoid', input_shape=(image_size,)))\n",
        "\n",
        "    for s in layer_sizes[1:]:\n",
        "        model.add(Dense(units = s, activation = 'sigmoid'))\n",
        "\n",
        "    model.add(Dense(units=num_classes, activation='softmax'))\n",
        "    return model\n",
        "\n",
        "def evaluate(model, batch_size=128, epochs=5):\n",
        "    model.summary()\n",
        "    model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "    history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=.1, verbose=False)\n",
        "    loss, accuracy  = model.evaluate(x_test, y_test, verbose=False)\n",
        "    \n",
        "    plt.plot(history.history['acc'])\n",
        "    plt.plot(history.history['val_acc'])\n",
        "    plt.title('model accuracy')\n",
        "    plt.ylabel('accuracy')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.legend(['training', 'validation'], loc='best')\n",
        "    plt.show()\n",
        "\n",
        "    print()\n",
        "    print(loss)\n",
        "    print(accuracy)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4NoXJwNr1dAf",
        "colab_type": "code",
        "colab": {},
        "outputId": "4b312fa7-164a-49f9-a42f-2a39f9b9ceda"
      },
      "source": [
        "for layers in range(1, 5):\n",
        "    model = create_dense([32] * layers)\n",
        "    evaluate(model)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_3 (Dense)              (None, 32)                25120     \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 10)                330       \n",
            "=================================================================\n",
            "Total params: 25,450\n",
            "Trainable params: 25,450\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "()\n",
            "0.48585037174224854\n",
            "0.8874\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_5 (Dense)              (None, 32)                25120     \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 32)                1056      \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 10)                330       \n",
            "=================================================================\n",
            "Total params: 26,506\n",
            "Trainable params: 26,506\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "()\n",
            "1.196845540046692\n",
            "0.7812\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_8 (Dense)              (None, 32)                25120     \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 32)                1056      \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 32)                1056      \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 10)                330       \n",
            "=================================================================\n",
            "Total params: 27,562\n",
            "Trainable params: 27,562\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "()\n",
            "2.1403482891082763\n",
            "0.4136\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_12 (Dense)             (None, 32)                25120     \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 32)                1056      \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 32)                1056      \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 32)                1056      \n",
            "_________________________________________________________________\n",
            "dense_16 (Dense)             (None, 10)                330       \n",
            "=================================================================\n",
            "Total params: 28,618\n",
            "Trainable params: 28,618\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "()\n",
            "2.2941267822265625\n",
            "0.1135\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yIFBwDlC1dAk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}